--- license: cc-by-nc-sa-4.0 task_categories: - image-segmentation tags: - medical - neuroimaging - stroke - CT - MRI - perfusion - ISLES - BIDS size_categories: - n<1K --- # ISLES'24 Stroke Training Dataset Multi-center longitudinal multimodal acute ischemic stroke training dataset from the ISLES'24 Challenge. ## Dataset Description - **Source:** [Zenodo Record 17652035](https://zenodo.org/records/17652035) (v7, November 2025) - **Challenge:** [ISLES 2024](https://isles-24.grand-challenge.org/) - **Paper:** [Riedel et al., arXiv:2408.11142](https://arxiv.org/abs/2408.11142) - **License:** CC BY-NC-SA 4.0 - **Size:** 99 GB (compressed) ## Overview 149 acute ischemic stroke training cases with: - **Admission imaging (ses-01):** Non-contrast CT, CT angiography, 4D CT perfusion - **Follow-up imaging (ses-02):** Post-treatment MRI (DWI, ADC) - **Clinical data:** Demographics, patient history, admission NIHSS, 3-month mRS outcomes - **Annotations:** Infarct masks, large vessel occlusion masks, Circle of Willis anatomy > **Note:** The ISLES'24 paper describes a training set of 150 cases; the Zenodo v7 training archive contains 149 publicly released subjects. ## Dataset Structure ### Imaging Modalities | Session | Modality | Description | |---------|----------|-------------| | ses-01 (Acute) | `ncct` | Non-contrast CT | | ses-01 (Acute) | `cta` | CT Angiography | | ses-01 (Acute) | `ctp` | 4D CT Perfusion time series | | ses-01 (Acute) | `tmax` | Time-to-maximum perfusion map | | ses-01 (Acute) | `mtt` | Mean transit time map | | ses-01 (Acute) | `cbf` | Cerebral blood flow map | | ses-01 (Acute) | `cbv` | Cerebral blood volume map | | ses-02 (Follow-up) | `dwi` | Diffusion-weighted MRI | | ses-02 (Follow-up) | `adc` | Apparent diffusion coefficient | ### Derivative Masks | Mask | Description | |------|-------------| | `lesion_mask` | Binary infarct segmentation (from follow-up MRI) | | `lvo_mask` | Large vessel occlusion mask (from CTA) | | `cow_mask` | Circle of Willis anatomy (multi-label, auto-generated from CTA) | ### Clinical Variables Clinical variables are extracted from per-subject XLSX files in the `phenotype/` directory: | Variable | Source File | Description | |----------|-------------|-------------| | `age` | demographic_baseline.xlsx | Patient age at admission | | `sex` | demographic_baseline.xlsx | Patient sex (M/F) | | `nihss_admission` | demographic_baseline.xlsx | NIH Stroke Scale score at admission | | `mrs_admission` | demographic_baseline.xlsx | Modified Rankin Scale at admission | | `mrs_3month` | outcome.xlsx | Modified Rankin Scale at 3 months (primary outcome) | ## Usage ```python from datasets import load_dataset ds = load_dataset("hugging-science/isles24-stroke", split="train") # Access a subject example = ds[0] print(example["subject_id"]) # "sub-stroke0001" print(example["ncct"]) # Non-contrast CT array print(example["dwi"]) # Diffusion-weighted MRI print(example["lesion_mask"]) # Ground truth segmentation print(example["nihss_admission"]) # NIH Stroke Scale at admission print(example["mrs_3month"]) # Modified Rankin Scale at 3 months ``` ## Data Organization The source data follows BIDS structure. This tree shows the actual Zenodo v7 layout: ``` train/ ├── clinical_data-description.xlsx ├── raw_data/ │ └── sub-stroke0001/ │ └── ses-01/ │ ├── sub-stroke0001_ses-01_ncct.nii.gz │ ├── sub-stroke0001_ses-01_cta.nii.gz │ ├── sub-stroke0001_ses-01_ctp.nii.gz │ └── perfusion-maps/ │ ├── sub-stroke0001_ses-01_tmax.nii.gz │ ├── sub-stroke0001_ses-01_mtt.nii.gz │ ├── sub-stroke0001_ses-01_cbf.nii.gz │ └── sub-stroke0001_ses-01_cbv.nii.gz ├── derivatives/ │ └── sub-stroke0001/ │ ├── ses-01/ │ │ ├── perfusion-maps/ │ │ │ ├── sub-stroke0001_ses-01_space-ncct_tmax.nii.gz │ │ │ ├── sub-stroke0001_ses-01_space-ncct_mtt.nii.gz │ │ │ ├── sub-stroke0001_ses-01_space-ncct_cbf.nii.gz │ │ │ └── sub-stroke0001_ses-01_space-ncct_cbv.nii.gz │ │ ├── sub-stroke0001_ses-01_space-ncct_cta.nii.gz │ │ ├── sub-stroke0001_ses-01_space-ncct_ctp.nii.gz │ │ ├── sub-stroke0001_ses-01_space-ncct_lvo-msk.nii.gz │ │ └── sub-stroke0001_ses-01_space-ncct_cow-msk.nii.gz │ └── ses-02/ │ ├── sub-stroke0001_ses-02_space-ncct_dwi.nii.gz │ ├── sub-stroke0001_ses-02_space-ncct_adc.nii.gz │ └── sub-stroke0001_ses-02_space-ncct_lesion-msk.nii.gz └── phenotype/ └── sub-stroke0001/ ├── ses-01/ └── ses-02/ ``` ## Citation When using this dataset, please cite: ```bibtex @article{riedel2024isles, title={ISLES'24 -- A Real-World Longitudinal Multimodal Stroke Dataset}, author={Riedel, Evamaria Olga and de la Rosa, Ezequiel and Baran, The Anh and Hernandez Petzsche, Moritz and Baazaoui, Hakim and Yang, Kaiyuan and Musio, Fabio Antonio and Huang, Houjing and Robben, David and Seia, Joaquin Oscar and Wiest, Roland and Reyes, Mauricio and Su, Ruisheng and Zimmer, Claus and Boeckh-Behrens, Tobias and Berndt, Maria and Menze, Bjoern and Rueckert, Daniel and Wiestler, Benedikt and Wegener, Susanne and Kirschke, Jan Stefan}, journal={arXiv preprint arXiv:2408.11142}, year={2024} } @article{delarosa2024isles, title={ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand?}, author={de la Rosa, Ezequiel and Su, Ruisheng and Reyes, Mauricio and Wiest, Roland and Riedel, Evamaria Olga and Kofler, Florian and others and Menze, Bjoern}, journal={arXiv preprint arXiv:2408.10966}, year={2024} } ``` If using Circle of Willis masks, also cite: ```bibtex @article{yang2023benchmarking, title={Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA}, author={Yang, Kaiyuan and Musio, Fabio and Ma, Yue and Juchler, Norman and Paetzold, Johannes C and Al-Maskari, Rami and others and Menze, Bjoern}, journal={arXiv preprint arXiv:2312.17670}, year={2023} } ``` ## Related Resources - [ISLES 2024 Challenge](https://isles-24.grand-challenge.org/) - [Zenodo Dataset (DOI: 10.5281/zenodo.17652035)](https://doi.org/10.5281/zenodo.17652035) - [Dataset Paper (arXiv:2408.11142)](https://arxiv.org/abs/2408.11142) - [Challenge Paper (arXiv:2408.10966)](https://arxiv.org/abs/2408.10966)